Military Operations Research最新文献

Optimal Routing Under Demand Surges: The Value of Future Arrival Rates 需求激增下的最优路线:未来到达率的价值

IF 0.7 4区管理学

Military Operations Research Pub Date : 2023-09-04 DOI: 10.1287/opre.2022.0282

Jinsheng Chen, Jing Dong, P. Shi

引用次数: 0

Demand Estimation Under Uncertain Consideration Sets 不确定考虑集下的需求估计

IF 0.7 4区管理学

Military Operations Research Pub Date : 2023-09-04 DOI: 10.1287/opre.2022.0006

Srikanth Jagabathula, Dmitry Mitrofanov, Gustavo Vulcano

{"title":"Demand Estimation Under Uncertain Consideration Sets","authors":"Srikanth Jagabathula, Dmitry Mitrofanov, Gustavo Vulcano","doi":"10.1287/opre.2022.0006","DOIUrl":"https://doi.org/10.1287/opre.2022.0006","url":null,"abstract":"In “Demand Estimation Under Uncertain Consideration Sets,” Jagabathula, Mitrofanov, and Vulcano investigate statistical properties of the consider-then-choose (CTC) models, which gained recent attention in the operations literature as an alternative to the classical random utility (RUM) models. The general class of CTC models is defined by a general joint distribution over ranking lists and consideration sets. Starting from the important result that the CTC and RUM classes are equivalent in terms of explanatory power, the authors characterize conditions under which CTC models become identified. Then, they propose expectation-maximization (EM) methods to solve the related estimation problem for different subclasses of CTC models, building from the provably convergent outer-approximation algorithm. Finally, subclasses of CTC models are tested on a synthetic data set and on two real data sets: one from a grocery chain and one from a peer-to-peer (P2P) car sharing platform. The results are consistent in assessing that CTC models tend to dominate RUM models with respect to prediction accuracy when the training data are noisy (i.e., transaction records do not necessarily reflect the physical inventory records) and when there is significant asymmetry between the training data set and the testing data set. These conditions are naturally verified in P2P sharing platforms and in retailers working on long-term forecasts (e.g., semester long) or geographical aggregate forecasts (e.g., forecasts at the distribution center level).","PeriodicalId":49809,"journal":{"name":"Military Operations Research","volume":"1 1","pages":""},"PeriodicalIF":0.7,"publicationDate":"2023-09-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81709717","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 3

Optimal Routing to Parallel Servers in Heavy Traffic 大流量下并行服务器的最优路由

IF 0.7 4区管理学

Military Operations Research Pub Date : 2023-08-16 DOI: 10.1287/opre.2022.0055

H. Ye

{"title":"Optimal Routing to Parallel Servers in Heavy Traffic","authors":"H. Ye","doi":"10.1287/opre.2022.0055","DOIUrl":"https://doi.org/10.1287/opre.2022.0055","url":null,"abstract":"Routing control is an important component in many engineering and management systems consisting of multiple and possibly heterogeneous servers. Imagine that upon the arrival of each job (or customer), a controller will evaluate the available (dynamic) state information and make a decision to dispatch the job to one of the servers. The state information can be queue length, arrival history, service history, and so on, depending on the nature of the application. How will the controller use the available state information to minimize the average waiting time an arriving job may experiences? In the paper, “Optimal Routing to Parallel Servers in Heavy Traffic,” Ye carries out the heavy traffic analysis to identify the routing policies that best use the available state information. For example, when there is no state information available for routing control, the best “blind” strategy is to dispatch the incoming jobs in a weighted round-robin fashion that exhibits certain form of the square-root rule. Although in the case that the job arrival history is available, the controller should use the information by closely chasing a kind of “arrival deviation,” which can reduce up to 50% of the waiting time compared with the best blind strategy. This study sheds new insights into the value of state information for routing control and provides new tools for engineering and service system design.","PeriodicalId":49809,"journal":{"name":"Military Operations Research","volume":"75 1","pages":""},"PeriodicalIF":0.7,"publicationDate":"2023-08-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89636518","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

The When and How of Delegated Search 委托搜索的时间和方式

IF 0.7 4区管理学

Military Operations Research Pub Date : 2023-08-14 DOI: 10.1287/opre.2019.0498

Saša Zorc, Ilia Tsetlin, Sameer Hasija, S. Chick

引用次数: 0

A Data-Driven Approach to Beating SAA Out of Sample 打败样本外SAA的数据驱动方法

IF 0.7 4区管理学

Military Operations Research Pub Date : 2023-08-11 DOI: 10.1287/opre.2021.0393

Jun-ya Gotoh, Michael Jong Kim, Andrew E. B. Lim

{"title":"A Data-Driven Approach to Beating SAA Out of Sample","authors":"Jun-ya Gotoh, Michael Jong Kim, Andrew E. B. Lim","doi":"10.1287/opre.2021.0393","DOIUrl":"https://doi.org/10.1287/opre.2021.0393","url":null,"abstract":"A Little Pessimism Goes a Long Way Data-driven optimization is concerned with finding a decision, using data and perhaps a model, that performs well when it is applied on a new unseen data point. Data-driven optimization is challenging because data are limited or the model is wrong or the environment in which the decision is being applied is different from the one in which the training data were collected. Distributionally robust optimization (DRO), a worst case optimization method for finding decisions that are insensitive to model error, can sometimes but not always deliver a decision that has a larger out-of-sample expected reward than the sample average approximation (SAA). “A Data Driven Approach to Beating SAA out of Sample” by Jun-ya Gotoh, Michael Kim, and Andrew Lim shows that if worst case (DRO) solutions fail at this task, then the solution of a best case distributionally optimistic optimization problem will do the job. As good as this sounds, there is a catch: whereas an optimistic decision might beat SAA, the improvement is very modest and comes at the cost of being much more sensitive to model misspecification than both the SAA and the DRO decisions. Moreover, it is easy to make a mistake: it can be difficult to determine with a modestly sized data set whether the best or worst case solution will have the higher expected reward than SAA. In summary, data driven optimization is a trade-off between maximizing the expected reward and controlling the sensitivity of this expectation to model misspecification. When both are considered, a little bit of pessimism goes a long way.","PeriodicalId":49809,"journal":{"name":"Military Operations Research","volume":"1 1","pages":""},"PeriodicalIF":0.7,"publicationDate":"2023-08-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82901563","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

Data-Driven Hospital Admission Control: A Learning Approach 数据驱动的住院控制:一种学习方法

IF 0.7 4区管理学

Military Operations Research Pub Date : 2023-08-10 DOI: 10.1287/opre.2020.0481

M. Zhalechian, Esmaeil Keyvanshokooh, Cong Shi, M. P. Van Oyen

引用次数: 1

Technical Note—Data-Driven Profit Estimation Error in the Newsvendor Model 技术笔记-数据驱动的报贩模型利润估计误差

IF 0.7 4区管理学

Military Operations Research Pub Date : 2023-08-09 DOI: 10.1287/opre.2023.0070

A. Siegel, Michael R. Wagner

引用次数: 2

Inventory Control and Learning for One-Warehouse Multistore System with Censored Demand 需求删减的一库多库系统的库存控制与学习

IF 0.7 4区管理学

Military Operations Research Pub Date : 2023-08-02 DOI: 10.1287/opre.2021.0694

Recep Yusuf Bekci, M. Gümüş, Sentao Miao

引用次数: 1

Competitive Algorithms for the Online Minimum Peak Job Scheduling 在线最小峰值作业调度的竞争算法

IF 0.7 4区管理学

Military Operations Research Pub Date : 2023-08-02 DOI: 10.1287/opre.2021.0080

Célia Escribe, Michael Hu, R. Levi

引用次数: 0

Gaussian Process-Based Random Search for Continuous Optimization via Simulation 基于高斯过程的随机搜索连续优化仿真

IF 0.7 4区管理学

Military Operations Research Pub Date : 2023-08-01 DOI: 10.1287/opre.2021.0303

Xiuxian Wang, L. Hong, Zhibin Jiang, Haihui Shen

{"title":"Gaussian Process-Based Random Search for Continuous Optimization via Simulation","authors":"Xiuxian Wang, L. Hong, Zhibin Jiang, Haihui Shen","doi":"10.1287/opre.2021.0303","DOIUrl":"https://doi.org/10.1287/opre.2021.0303","url":null,"abstract":"A gaussian process-based random search framework for continuous optimization via simulation Stochastic optimization via simulation (OvS) is widely used for optimizing the performances of complex systems with continuous decision variables. Because of the existence of simulation noise and infinite feasible solutions, it is challenging to design an efficient mechanism to do the searching and estimation simultaneously to find the optimal solutions. In “Gaussian process-based random search for continuous optimization via simulation,” Wang et al. propose a Gaussian process-based random search (GPRS) framework for the design of single-observation and adaptive continuous OvS algorithms. This framework builds a Gaussian process surrogate model to estimate the objective function value of every solution based on a single observation of each sampled solution in each iteration and allow for a wide range of sampling distributions. They prove the global convergence and analyze the rate of convergence for algorithms under the GPRS framework. They also give a specific example of GPRS algorithms and validate its theoretical properties and practical efficiency using numerical experiments.","PeriodicalId":49809,"journal":{"name":"Military Operations Research","volume":"18 1","pages":""},"PeriodicalIF":0.7,"publicationDate":"2023-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89467153","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0